eprintid: 3561
rev_number: 12
eprint_status: archive
userid: 408
dir: disk0/00/00/35/61
datestamp: 2019-10-01 03:00:55
lastmod: 2019-10-01 03:00:55
status_changed: 2019-10-01 03:00:55
type: article
metadata_visibility: no_search
creators_name: Parlavantzas, Nikos
creators_name: Pham, Manh Linh
creators_name: Morin, Christine
creators_name: Arnoux, Sandie
creators_name: Beaunée, Gaël
creators_name: Qi, Luyuan
creators_name: Gontier, Philippe
creators_name: Ezanno, Pauline
creators_id: nikos.parlavantzas@irisa.fr
creators_id: linhmp@vnu.edu.vn
creators_id: Christine.Morin@inria.fr
creators_id: sandie.arnoux@inra.fr
creators_id: gael.beaunee@inra.fr
creators_id: qiluyuan@gmail.com
creators_id: philippe.gontier@oniris-nantes.fr
creators_id: pauline.ezanno@oniris-nantes.fr
title: A Service-based Framework for Building and Executing Epidemic Simulation Applications in the Cloud
ispublished: inpress
subjects: IT
subjects: Scopus
subjects: isi
divisions: FIMO
divisions: fac_fit
keywords: Cloud computing; High Performance Computing; Simulation models; Epidemic simulation
abstract: The cloud has emerged as an attractive platform for resource-intensive scientific applications, such as epidemic simulators. However, building and executing such applications in the cloud presents multiple challenges, including exploiting elasticity, handling failures, and simplifying multi-cloud deployment. To address these challenges, this paper proposes a novel, service-based framework called DiFFuSE that enables simulation applications with a bag-of-tasks structure to fully exploit cloud platforms. The paper describes how the framework is applied to restructure two legacy applications, simulating the spread of bovine viral diarrhea virus and Mycobacterium avium subspecies paratuberculosis, into elastic, cloud-native applications. Experimental results show that the framework enhances application performance and allows exploring different cost-performance trade-offs while supporting automatic failure handling and elastic resource acquisition from multiple clouds.
date: 2019-09
date_type: completed
publisher: John Wiley & Sons Inc
contact_email: linhmp@vnu.edu.vn
full_text_status: public
publication: Concurrency and Computation: Practice and Experience
refereed: TRUE
issn: 1532-0626
referencetext: 1. Eriksson H, Timpka T, Spreco A, Dahlstrom O, Stromgren M, Holm E. Dynamic Multicore Processing for Pandemic Influenza Simulation. In: AMIA Annual Symposium Proceedings; 2016: 534-540.
2. Pham LM, Parlavantzas N, Morin C, et al. DiFFuSE, a Distributed Framework for Cloud-Based Epidemic Simulations: A Case Study in Modelling the Spread of Bovine Viral Diarrhea Virus. In: 2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom); 2017:304-313
3. Pham LM, Parlavantzas N, Morin C, et al. DiFFuSE, a Distributed Framework for Cloud-based Epidemic Simulations: a Case Study in Modelling the Spread of Bovine Viral Diarrhea Virus. Research Report RR-9094, Inria Rennes - Bretagne Atlantique; Rennes: 2017.
4. Bhatele A, Yeom JS, Jain N, et al. Massively Parallel Simulations of Spread of Infectious Diseases over Realistic Social Networks. In: 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID); 2017: 689-694
5. G. Martin MM, Carretero J. Towards Efficient Large Scale Epidemiological Simulations in EpiGraph. Parallel Computing 2015; 42(C): 88–102.
6. Perumalla KS, Seal SK. Reversible Parallel Discrete-Event Execution of Large-Scale Epidemic Outbreak Models. In: 2010 IEEE Workshop on Principles of Advanced and Distributed Simulation; 2010: 1-8
7. Rao DM, Chernyakhovsky A. Parallel simulation of the global epidemiology of Avian Influenza. In: 2008 Winter Simulation Conference; 2008:
1583-1591
8. Bisset KR, Chen J, Deodhar S, Feng X, Ma Y, Marathe MV. Indemics: An Interactive High-performance Computing Framework for Data-intensive Epidemic Modeling. ACM Transactions on Modeling and Computer Simulation 2014; 24(1): 4:1–4:32. doi: 10.1145/2501602
9. Zou P, Lu sY, Wu dL, Chen lL, Yao pY. Epidemic simulation of a large-scale social contact network on GPU clusters. SIMULATION 2013; 89(10):1154-1172. doi: 10.1177/0037549713482026
10. Holvenstot P, Prieto D, Doncker dE. GPGPU parallelization of self-calibrating agent-based influenza outbreak simulation. In: 2014 IEEE High Performance Extreme Computing Conference (HPEC); 2014: 1-6
11. Eriksson H, Raciti M, Basile M, et al. A Cloud-Based Simulation Architecture for Pandemic Influenza Simulation. In: AMIA Annual Symposium Proceedings; 2011: 364-73.
12. Sukcharoen P, Pumma S, Mongkolsermporn O, Achalakul T, Li X. Design and analysis of a cloud-based epidemic simulation framework. In:2012 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology; 2012: 1-4.
13. Price RC, Pettey W, Freeman T, et al. SaTScan on a Cloud: On-Demand Large Scale Spatial Analysis of Epidemics. In: Online Journal of Public Health Informatics; 2(1): ojphi.v2i1.2910; 2010.
14. Haris M, Manzoor MS. Spatiotemporal Study of Dengue Virus Infection via Cloud Based Framework. International Journal of Advanced Research in Computer Science and Software Engineering 2016; 6(6): 155-161.
15. Benedictis AD, Rak M, Turtur M, Villano U. A Framework for Cloud-aware Development of Bag-of-tasks Scientific Applications. International Journal of Grid and Utility Computing 2016; 7(2): 130–140.
16. Agarwal D, Prasad SK. AzureBOT: A Framework for Bag-of-Tasks Applications on the Azure Cloud Platform. In: 2013 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum; 2013: 2139-2146.
17. Król D, Orzechowski M, Kitowski J, Niethammer C, Sulisto A, Wafai A. A Cloud-Based Data Farming Platform for Molecular Dynamics
Simulations. In: UCC 2014; 2014: 579-584
18. Thai L, Varghese B, Barker A. A survey and taxonomy of resource optimisation for executing bag-of-task applications on public clouds. Future
Generation Computer Systems 2018; 82: 1 - 11.
19. Ezanno P, Fourichon C, Viet AF, Seegers H. Sensitivity analysis to identify key-parameters in modelling the spread of bovine viral diarrhoea virus in a dairy herd. Preventive Veterinary Medicine 2007; 80(1): 49–64.
20. Garcia A, Shalloo L. Invited review: The economic impact and control of paratuberculosis in cattle. Journal of dairy science 2015; 98(8): 5019–5039.
21. Qi L, Vergu E, Dutta BL, Ezanno P. Modeling bovine viral diarrhea virus (BVDV) spread between dairy cattle farms at a regional scale: relative contribution of two between-herd transmission pathways.; 2017. Presented at the Society for Veterinary Epidemiology and Preventive
Medicine (SVEPM), Inverness, GB.
22. Ezanno P, Fourichon C, Seegers H. Influence of herd structure and type of virus introduction on the spread of bovine viral diarrhoea virus (BVDV) within a dairy herd. Veterinary Research 2008; 39(5): 39.
23. Beaunée G, Vergu E, Ezanno P. Modelling of paratuberculosis spread between dairy cattle farms at a regional scale. Veterinary Research 2015;
46(1): 295.
24. Beaunée G, Vergu E, Joly A, Ezanno P. Controlling bovine paratuberculosis at a regional scale: Towards a decision modelling tool. Journal of theoretical biology 2017; 435: 157–183.
25. Marcé C, Ezanno P, Seegers H, Pfeiffer D, Fourichon C. Within-herd contact structure and transmission of Mycobacterium avium subspecies paratuberculosis in a persistently infected dairy cattle herd. Preventive veterinary medicine 2011; 100(2): 116–125.
26. PaaSage. https://www.paasage.eu [21 May 2019]; .
27. Parlavantzas N, Pham LM, Sinha A, Morin C. Cost-Effective Reconfiguration for Multi-Cloud Applications. In: 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP); 2018: 521-528
28. Nanomsg. http://nanomsg.org [21 May 2019]; .
29. GWDG. https://www.gwdg.de [21 May 2019]; .
30. Grid5000. https://www.grid5000.fr [21 May 2019]; .
31. BioEpAR. https://www6.angers-nantes.inra.fr/bioepar [15 July 2019]; .
funders: FEDER
funders: Agreenskills plus
projects: ANR-10-BINF-07
projects: FP7-317715
citation:   Parlavantzas, Nikos and Pham, Manh Linh and Morin, Christine and Arnoux, Sandie and Beaunée, Gaël and Qi, Luyuan and Gontier, Philippe and Ezanno, Pauline  (2019) A Service-based Framework for Building and Executing Epidemic Simulation Applications in the Cloud.  Concurrency and Computation: Practice and Experience .    ISSN 1532-0626    (In Press)  
document_url: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3561/1/CPE-18-1433.R2_Proof_hi.pdf